十边形企业级AI应用构建指南

这让我感触很深，因为我一直觉得自己已经对AGI深信不疑了，但还真没从这个角度想过。

It really hit me because I thought I was AGI pilled, but I'd really not thought about from that perspective.

我挺好奇的。

And I'm curious.

咱们常说产品会越来越好，对吧？

You know, we talk about the product improving.

是吧？

Right?

能给我们具体讲讲吗？

Can you make that concrete for us?

比如，在优化产品给客户带来价值时，有没有哪些让你或客户惊呼‘哇塞’的时刻？如果你愿意分享的话，就是那种让人惊叹的瞬间。

Like, what are some of the oh shit moments as you improve your product for your customer, either from your end or your customer's end, if you can share on, like, oh my god.

我没想到AI竟然能做到这个程度。

I didn't realize AI could do that.

当然，接下来我也想听听你对AGI的看法，以及你觉得它实现的时间线大概是怎样的。

And then, of course, I'm going to ask you your thoughts on AGI and what that timeline looks like.

因为你深入接触了那些使用AI的企业，所以你的视角可能跟我们不一样。

Because you're in the nitty gritty trenches of the enterprises using AI, so you may have a different perspective than, you know, we do.

我想说的第一点是，我相信AGI之后依然会存在各种职业。

The first thing I wanna say is I'm I'm, like, certain there will be careers after AGI.

原因在于，我们大部分的工作，其实都是真的。

The reason for that is, like, most of our Yeah.

工作，确实是真的。

Jobs Yeah.

毫无疑问，这些工作都是真实存在的。

For sure are for jobs.

说实话，大多数工作都是人为创造出来的。

We're kind of, like, made up, to be honest.

大部分工作都是

Like, most jobs are

人为创造出来的。

made up.

销售。

Sell,

杰西。

Jesse.

那确实是个正经工作。

That's a very real job.

除非你是搞基建或者种粮食之类的。

Unless you're like building infrastructure or like growing food or something.

大多数工作其实就像是在其他基础之上层层叠加出来的抽象层。

Most jobs are kind of like layers of abstraction built on top of like other stuff.

对吧？

Right?

这并不意味着

And that that isn't to

这些工作没有价值。

say like the jobs aren't valuable.

其实就是它们有点儿是人为构建出来的。

It's just they're kind of made up.

所以当AGI真的来了，它会改变人的工作，但人们不会就此闲着，因为你终究还是要为其他人做事，不管做什么。

So when AGI is here, like, it'll change people's jobs, but everyone people will stop jobs because you're still gonna do things for other humans and and whatever.

所以我并不觉得AGI来了之后，职业就会消失。

So I I don't really believe that careers will be gone after AGI.

我不认为人们会整天无所事事地坐着。

I don't think people are just gonna be sitting around.

我有个观察，对我而言，那肯定是Duet。

I'll say one observation, which is, like, for me, it was definitely Duet.

在产品早期，我们开发的时候，核心问题还是销售驱动的——我们接触了很多客户，他们说，你们做的这个东西，对我们来说最有价值的地方在于，能直接面对客户，让他们跟系统对话，体验更好，而且我们自己运营起来也轻松多了。

So early days when we were building the product, right, like, the the core problem we're solving, again, being back to sales led, is we talked to a bunch of customers, and they're like, yeah, the value we wanna get out of what you're building for us is that, you know, we can put it in front of customers, they can have conversations, and it's giving them a much better experience, and also it's way easier for us operationally, right?

相当于你既降低了成本，又让客户更满意。

It's like you're saving cost, you're making customers happier.

所以我们最早做的那个智能体就是这个，也是我们头一两年专注的核心智能体。开发过程中，我们做了大量工作：得写各种操作流程，还自己搞了一套流程格式，叫‘智能体操作规程’，用来教AI怎么做事；还得开发一堆工具，让这些流程能调用系统、获取API之类的；之后还要设计大量测试，模拟各种可能的情况；等上线后，我们还得手动去读客户的对话记录。

So that's the first agent that we built, and that was the core agent we worked on for the first year, a year and two years, and the agent on our end, when we were building it, consisted of a ton of stuff, like we would have to write these procedures, and we kind of created our own format of procedures, we call them agent operating procedures that teach the AI how to do things, we have to write these tools that the procedures can use to access systems and pull APIs and whatever, and then after that, we need to, like, create all these tests to make sure that this thing is working well, and that you can kinda simulate all these different situations, and afterwards, once they're in production, like, would manually be reading conversations.

对吧？

Right?

尽管我们做的核心产品本身就是一个智能体，但背后要做的工作量大得惊人。

There's like a ton of work that goes into it, even though the core product we're building is is itself an agent.

Duet其实是个独立的智能体，它更大、更慢，但任务就是干我刚才说的那些事。

And so what a duet is is it's kind of a separate agent, it's like a second agent that's much bigger and much slower, but its job is to do all the tasks I just described.

现在不用我们自己写AOP、开发系统集成和工具、写测试、监控对话了，Duet全包了，对吧？

So now, instead of us having to write these AOPs, and write these integrations and tools into their systems, and write these tests, and monitor the conversations, do I just does all of that, right?

它就像是第二个智能体，聪明到能把这些事全搞定。

So it's like a, it's a second agent that is smart enough to do all of these things.

这感觉特别神奇，因为你只要跟它说：‘我目前啥都没建，但这里有几段对话记录和一些文档。’

And it just feels very magical, because you can just literally tell it like, hey, I have nothing built yet so far, but here's a bunch of transcripts I have, and here's some documentation.

然后你让它自己琢磨出最合适的执行流程。

You go figure out the best way to do all these procedures I want.

它就会自己去完成，还会顺手写出配套的测试和模拟场景。

And it'll go do it, and then of its own accord, it'll also write the tests and simulations that go along with those.

等你做完这些，真正把系统推到客户面前时，它就会自动监控所有对话，识别出哪些地方表现得好，哪些地方出了问题。

And once you're done with that, and you actually put it in front of customers, it'll be the one that's monitoring all the conversations, and it'll flag things where things are going well or poorly.

它会说：去读这1000段对话吧，我发现我们在这一个话题上做得特别差，我已经帮你拟好了改进方案。

It'll say, yeah, read these 1,000 conversations, and actually, there's this one topic that we do really poorly on, and I've noticed that, and I've also drafted these improvements for you.

这简直太惊人了。

So it's like Wow.

一个代理就能完成所有这些事。

It's like one agent that can do all of that.

这确实很神奇，因为当初公司刚成立的时候，这根本不可能实现。

And so that's very magical because, well, of all, it was not possible when we first started the company.

直到推理模型大幅进步，这才成为可能。当然，Anthropic和OpenAI主要是在为像Cloud Code这样的场景优化这些模型，但它们对Duet这类应用也同样出色。

It only became possible when all the reasoning models got better, and of course, Anthropic, OpenAI are making these reasoning models mostly for like, the Cloud codes of the world, but they are also really good for, like, Duet, for example.

那一刻，真的让人忍不住感叹：哇。

And that was kinda like a moment where it's like, oh, wow.

首先，你能明显看到模型随着时间不断进步。

Like, first of all, you can just see the improvement over time of the models.

其次，它还能完成这些原本没人会想到AI能同时做到的任务，但它不仅能做到，还做得非常好。

And two, it can do all these tasks where it just would not be able you you would not have expected AI to be able to do all these tasks at once, but it can and can do them very well.

我觉得那一刻特别真实，就是——

And I think that's that was a very visceral moment of, hey.

哇。

Wow.

模型进步得太快了，而且变得越来越通用。

The models are getting a lot better, and they're becoming very generalized.

你明白吗？

Know?

它们能干这么多事。

Like, they can do all these things.

很明显，这些模型根本没专门训练过我们这种任务，比如写流程和写测试，但它们依然表现得很出色。

Like, clearly, models were not trained on our specific task, which is, you know, writing these procedures and and writing these tests, but they're still good at

还有，你之前问过，我们是怎么想到这些的，产品又是怎么一步步改进的？

And also, to your other question of how, you know, how how did we come up with all this, and how how did the product improve?

杰西刚才提到的每一件事，都是前线同事实际做出来的，我们后来才想办法把它产品化。

Every single thing that Jesse just talked about was the result of forward deployed people doing things and us figuring out how to productize it.

对吧？

Right?

比如说，我们发现，

So for instance, we realized that, hey.

每次进入新客户时，都得花大量时间手动编写AOP文档。

When we go into a new customer, we need to spend all this time writing up the AOPs manually.

然后我们就说，天啊，

And we're like, wow.

这花的时间也太多了吧。

This is quite a lot of time.

怎么才能把这件事产品化呢？

How do we productize this?

于是我们就把它做进了Duet。

And we built that into Duet.

然后第二部分，我们叫Duet自动模式，是在Duet上线之后，大家开始用它来写内容，那时候我们就说，哇。

And then the second part, which we call Duet autopilot, was once we built Duet, you know, people were using Duet to write things up, and then we're like, oh, wow.

但还是得花很多时间去不断优化这个智能体。

There's still a lot of time that goes into iterating upon the agent.

对吧？

Right?

等它上线后，还得去复盘对话、琢磨怎么改进，这时候我们就想，太好了。

Once it goes live, like, reviewing conversations, figuring out how to improve it, and we're like, great.

干脆把这个也做成一个自动化功能吧。

Let's productize that as do an autopilot.

事实上，AOP本身也是源于同样的情况，因为在AOP出现之前，你得把所有这些流程都用代码写出来。

In fact, AOPs themselves were a result of this exact scenario because before AOPs, you would have to write all these procedures in code.

没错。

Mhmm.

后来我们发现，要写出这么多代码，其实耗费了大量前线工程师的精力。

And then we found that, oh, it's taken a lot of forward deployed engineering work to write all these things in code.

真厉害。

Wow.

如果我们能用plain text来产品化，是不是会简单高效得多？

Wouldn't it be so much easier and efficient if we could productize it by writing it in plain text?

没错。

Right.

我们思考产品改进、前线工程投入，都是围绕着一个核心：如何把前线工作成果产品化，让工程师和团队里任何面向客户的同事不必再深度介入。

So the way we think about product improvements, forward deployed engineering investment, everything is built around what can we productize from forward deployed work so that engineers and any kind of customer facing resources on our team don't need to be kind of as heavily involved.

我问一下。

I ask

我有个直截了当的问题想问你们，关于这个思路。

you guys kind of a blunt question on this line of thinking?

我知道我们已经聊过，为什么OpenAI和Anthropic不会是最后的初创公司。

So I know we've already talked about why open- and Anthropic won't be the last startups.

你也谈过为什么市场容得下更多专注特定场景的公司，但你们肯定也经历过那种‘天啊’的时刻——眼看着这些大厂进步神速，模型越来越强。

And you've talked about like why there's room to have like much more specific use cases and specific companies, but you've also got had like, oh shit moments where you're like, these labs are getting so much better and the models are getting so much better.

从长远来看，假设我们真的实现了通用人工智能，模型能做今天根本想象不到的各种事情。

And so long term, let's say we hit AGI and the models can do all sorts of things we can't even imagine today.

到那时候，Decagon的竞争优势到底还能剩下什么？

What's Decagon's moat at the end of the day after all of that?

十年后，Decagon凭什么还能存在？

Why does Decagon 10 from now still have a right to exist?

我觉得短期内，关键就在于我们能和企业资源深度整合。

I think in the short term, actually, it is the ability to work with enterprise resources.

我的意思是，现在模型的能力远超企业在实际中对它们的使用程度。

And what I mean by this is that the capability of models today is far greater than they are being used for within the enterprise.

对吧？

Right?

你不可能直接把一个模型扔进企业，让它随便访问所有内部数据，然后指望它自己把一切都搞定。

By which you can't just take a model and say, I'm just gonna give this model access to everything within the enterprise, and I'll it'll just figure everything out.

对吧？

Right?

实际上，事情根本不是这么运作的。

Like, that's practically not how these things work.

所以，要想让这类模型在企业里真正落地，咱们先假设每个模型都完美无缺，从不出错。

So to make models like this deployable within the enterprise, right, like, let's assume that every model is, like, just perfect and makes no mistakes.

但要让这种系统在企业里用起来，你得先想清楚：

But to make something like this deployable within the enterprise, you need to say, okay.

得有个办法明确告诉模型哪些能做、哪些不能做，确保它绝不会犯致命的错误。

I need a way to be able to tell the model what it can and cannot do and make sure that it cannot do anything, like, catastrophically wrong.

还得有个机制，让企业里成百上千的人能一起协作，确保这个智能体在他们熟悉的场景里表现正常。

Then I need a way to make sure that, you know, hundreds of people within the enterprise can collaborate to make sure that the agent is behaving as expected in the use cases in which they're experts.

还得有办法测试模型，确认它不会触碰任何合规红线，同时验证它真的好用。

Then I need a way to be able to test this model and make sure that it doesn't cross any regulatory lines that I have and test it, make sure it works well.

还得能分析数以百万计的对话，从中提炼出有价值的信息，分享给其他团队。

Then I need a way to look over the millions of conversations that happen for me to extract insights for the rest of my teams.

所以，要让这些模型在企业里真正落地，必须搭建一套完整的基础设施和软件系统，才能和公司现有的老旧系统无缝对接。

So there's a lot of just infrastructure and software that you need to build around these models to make them deployable within an enterprise, to make them work with all the legacy systems that these companies have.

所以我认为，在未来几年里，这是这些模型能够顺利运行最关键的能力。

And so I think for the next few years, that's probably going to be the primary thing that these models need to be able to work.

等这项技术变得普及了，到时候智能体能动态构建这些系统，具体会怎样发展，我现在还说不准，但三年后肯定会有很大变化。

Now once that gets commoditized because the agents can build that on the fly, that I don't know and will progress in in three years from now.

听你们这么一说，很明显你们非常懂怎么把AI卖给企业客户。

The way, I think just listening to you guys, it's very clear that you guys deeply understand how to sell AI to the enterprise.

我说的不只是中型市场或者刚上市的公司。

And I don't just mean, you know, mid market, newly IPO ed companies.

我是说那些全球最大的企业。

I'm talking about some of the largest companies in the world.

这特别有意思，因为你们两个人技术背景都很强，同时又特别擅长市场开拓。

And I think this is particularly interesting because you you both are very, very technical, but also go to market animals.

所以我想聊聊这方面。

So I wanna talk a little bit about that.

但感觉你们已经把原本像是大卫对抗多个歌利亚的市场格局，变成了你们和Sierra之间的双雄对决。

But, you know, it just feels like you've turned what maybe started as feeling more like a David and Goliath with multiple Goliaths market dynamic to really a two horse race between you and Sierra.

所以能不能再多讲讲，为什么世界上一些最大的企业会选择和你们合作？

So share a little bit more about why some of the largest enterprises in the world are buying from you guys.

对我们来说特别令人惊讶的是，人们研究应用软件已经十多年了。

And I think what's really remarkable to us is people have, you know, studied application software for, you know, over a decade.

你们的销售周期快得惊人。

The sales cycles are crazy fast.

我猜你们当然希望更快，但通常来说，像这么大的企业，根本不会这么快签这么大的合同。

I mean, I'm sure you guys, with urgency, want them to be faster, but, like, usually you don't sell contracts that big to enterprises that big.

对吧？

Right?

一般这种单子，光走流程就得两年。

That takes two years sometimes.

这个问题有点长，但你们是怎么做到商业上这么快成长的？

So maybe just share a little bit more on it's a long question, but like, how did you grow that commercial?

你们一开始创业的时候，就是带着这个思路来的吗？

Did you come with that into founding the business?

这些大公司为什么会对你们的产品如此认同，让你们能这么快地切入？

And what's resonating with these large companies that enables you to get in and move so quickly?

是的。

Yeah.

我们对Sierra以及其他这个领域的玩家都抱有很高的尊重，尤其是那些大型平台。

I mean, we were we have a lot of respect for for Sierra and also just, you know, other folks in the space, generally, the big platforms.

这些平台本身运作起来确实慢一些，但我们接触过不少他们的团队，都是很专业的，同时要兼顾太多事情。

Like, they all I think the platforms themselves move a bit slower, but I think that we've met a lot of those teams, they're very competent teams, they're optimizing for a lot of different things at once.

比如Decagon和Sierra，我们最近的一个客户就是从Sierra转过来的，我们问他们原因时，发现还是跟部署模式有关。

Decagon versus Sierra, mean, our most recent customer actually turned off to Sierra to come to Decagon, and sort of the reasoning when we asked them was, it kinda goes back to this deployment model.

他们之前用Sierra的时候，主要依赖全职人员，整个流程就像个黑箱——虽然那些全职人员很优秀，但每件事都得通过他们，想新建一个用户流程，或者想深入了解对话里的细节，都得等他们。

It's like, you know, when they worked with Sierra, it was mostly FTEs, and it just felt like a black box where, you know, the the FTEs were good, but they had to go through the FTEs for everything, so to build new journeys or to even get, like, a deeper understanding of what was happening in the conversations.

久而久之，这种流程就严重拖慢了他们的响应速度。

And then over time, that that kinda just create a lot of drag on how quickly they could move.

对吧？

Right?

所以最初部署的时候效果不错，但随着时间推移，那些全职人员可能被调去干别的了，导致他们花很长时间才能弄清楚发生了什么，更别提去设计新的流程了。

So in the initial deployment, that was good, but then over time, maybe the FTEs were staffed to other things, and it just took them a while to get insight into what was happening and then build out new journeys.

所以这一年里，他们大概只搞出了三个新流程，据他们说。

So over the course of the year, they maybe built out, I think they said three.

他们来找我们，正是因为有这种挫败感，他们希望换一种模式——更产品化一些。这正好契合我们的理念：我们主打产品驱动，哪怕我们亲自帮忙，哪怕我们驻场支持，核心目标也是帮他们打造一个能自己掌控、快速迭代的产品。

And so the reason they came to us is because they had that frustration, and they wanted to kind of have a different model where it was a lot more productized, right, back to us being very product driven in our vision, they should have a core product that even if we're there helping them, even if we are forward deployed, it is like an in service of helping them build a product that they can use themselves and iterate really fast and kind of have everything in their control.

对吧？

Right?

所以我们喜欢管这叫‘玻璃盒子’模式，而不是‘黑盒子’。

So we we like to call this like a glass box approach instead of a black box.

结果呢，差不多一个月内，他们就在Decagon上快速上线了七个新流程。

And and, yeah, so within basically a month, they spun up, like, seven new journeys on Decagon.

真厉害。

Wow.

挺有意思的。

Interesting.

主要是而且

Mostly And

它花了

it had

一年才做出三个吗？

taken three a year to get three?

是的。

Yeah.

明白了。

Okay.

有意思。

Interesting.

是零个。

It was zero.

所以这完全是迭代速度的问题。

So it's just kind of the speed of iteration.

有些团队会特别喜欢这种方式。

Like, some teams will really like this.

嘿，

Like, hey.

我们能自己掌控一切。

We have control of it.

我们的团队，尤其是非技术人员，可以随时参与进来，理解对话中的内容。

We our teams, especially our nontechnical people, can come in and do things, they understand what's happening in the in the conversations.

当然，也可能有其他团队更喜欢那种‘你们全包了’的模式。

And maybe there's other teams out there that actually do like the, like, hey, you guys do everything for us, and that approach.

但这就是双方做法的差异，也正因如此，我们在这一点上取得了不少成功。

But that's kind of the difference in their approaches, and that's why we've been, I would say, having a lot of success there.

再扩大来看，就是面向企业客户的销售。

And then zooming out, just selling to the enterprise.

说实话，我和阿什温以前都没做过企业销售，但巧合的是，我们都觉得销售挺有意思的。

Yeah, mean, is something that's, you know, Ashwin and I have never sold to the enterprise before, and I think it just so happens that both of us find sales exciting.

对企业市场，我们上手得挺快的，说实话，很多东西都是顺其自然的，因为这个领域本身就很热，跟客户聊的时候，我们根本不用费劲说服他们投资，重点是让他们明白——我们才是最适合他们的合作伙伴。

And the enterprise, was kind of a quick learning curve for us, so I don't think I think a lot of it honestly came kind of naturally, just because our space is so hot, and generally in these conversations, we're not really having to convince people to invest in this space, it's more of, hey, we're the right approach for you, so you can partner with us.

至于企业客户，关键在于理解他们的组织结构，真正站在他们的角度，明白他们看重什么、担心什么。

And, yeah, with the enterprise, it's really just about navigating the orgs and really having empathy for what they value and what they're afraid of.

所以，归根结底，我们还是销售驱动的，从一开始我们就决定，市场推广要做得特别强，而且要反过来影响产品设计。虽然我们信奉产品导向，但绝不会闭门造车，随便开发一些没人要的功能。

So, yeah, I mean, we just, back to being sales led, right, like, we always from the beginning were like, hey, we're gonna be, like, extremely strong on the go to market side, and that's gonna inform the product, even though we have this product driven philosophy, like, we don't wanna just be dreaming up random products to build.

我们从一开始就带着这种基因，早期更是拼了命地往前冲。

So we always had that DNA, and then in the early days, we were just, yeah, pushing really hard.

我还想说，我们运气不错，早期就搭起了一支非常出色的销售团队，里面人才济济，这让我们在接触大企业时有了很大的优势。

And I think I also wanna say, I think we got kinda blessed with a really strong early sales team, and we have a lot of really talented people in that group, and that helped us really get leverage as we were talking to, you know, the big enterprises.

我记得，有些人在早期甚至主动找上门来申请合作。

Some of whom cold applied to you guys in the early days, I remember.

没错。

Yes.

对。

Yeah.

对。

Yeah.

没错。

Yeah.

主动找上门来的。

Cold cold applied.

因为我觉得

Because I think

他们当时已经在做相关领域了，远远地看到Decagon刚开始做这些事

they were working in the in the space already, and they they were seeing from afar, like, what Decagon was starting

想做的事。

to do.

是的。

Yeah.

对。

Yeah.

他们中有些人原本的销售背景并不传统。

So some of them had, like, nontraditional sales backgrounds.

你知道的。

You know?

他们的销售背景确实不传统。

So they had nontraditional sales backgrounds.

他们是转行来做销售的。

They're coming into sales.

早年我们团队里很多都是常春藤盟校的运动员，大概吧。

The other profile we had a lot of in the early days were just, like like Ivy League athletes, I guess.

这些背景对团队来说是个不错的基础。

And those profiles were kind of a good foundation for the group.

我们得快速扩张团队，这从来都不容易。

And, you know, we've had to scale that team really fast, is never easy.

在销售支持和组织架构上，我们还有很多地方在追赶，但因为销售一直是我们最重视的环节，整个公司都知道——一切从销售开始，然后辐射到其他部门，所以我们始终保持着这种专注。

So there are things that we're still trying to catch up on in terms of enablement and org structure, but because we've always had that intensity on the sales side, and the whole company knows that, like, you know, everything starts with sales and it kinda propagates back, you know, we've always had that focus.

另外我觉得帮了我们大忙的一点，就像你之前提到的，我们是怎么这么快就签下了这些大单，其实是因为我们一直在思考，怎么把部分流程产品化，融入企业客户的应用场景。

The, you know, the other thing I think that helped us a lot, you know, to your earlier point about how did you get some of these large deals closed so quickly was, I think we were very curious about how we could productize parts of it within the enterprise.

确实如此。

Yeah.

对吧？

Right?

我们可不是那种只丢个产品过去，然后等一年才有人理你的公司。

By which I mean, we aren't a company that just says, hey, here's a product, we'll throw it over the wall, and you get it a year later.

因为在很多大企业里，除了‘这产品能不能用’，他们更关心的是‘我到底能不能真正用起来’。

Because within a lot of these enterprises, the question they have internally, in addition to will this product work for me, is can I actually get this live?

对吧？

Right?

尤其像金融行业这种受监管的领域，实际落地的难度要大得多。

And within a lot of these enterprises, it's actually complicated, especially if you're in financial services and you're regulated, for instance.

所以我们花了很多时间，把从初次接触到完全上线的整个流程都梳理得清清楚楚，这样每次走进一家企业，都能详细地告诉他们：从今天这次会面开始，每一步怎么走，最终怎么实现全面上线。

So we actually spent a lot of time sort of mapping out that part of the journey very well so that when we walk in to one of these enterprises, we can walk them through in very granular detail how we go from this first meeting today to going live at a 100%.

所以对于你们这样的公司，这个阶段模型风险流程大概就是这样的。

So at this stage for a company like you, this is what your model risk process is likely to be.

你们的测试流程应该按这个方式来安排。

This is how your testing process should look like.

初期上线我们就这样来做。

This is how we should do the initial rollout.

一旦发现问题，我们就这么捕捉、修复，并确保不会再犯同样的错误。

This is how we should catch any issues that come up and how we're going to fix them and how we'll ensure that they don't happen again.

我们所销售的产品和技术部分当然很重要。

So the product and technology part of what we sell is important.

但对于这些大公司来说，帮他们理清如何真正落地并规模化应用，同样关键。

But for these large companies, equally important is us helping them think through the process to actually get this deployed and at scale.

因为我觉得，很多科技公司在向企业销售时，常常忽略了这一点。

Because I think that is something that often gets overlooked by by tech companies selling into enterprise.

是的。

Yeah.

当然了。

For sure.

顺便说一句，我亲自接触过你们早期市场团队的不少人，确实能感受到他们的实力。

And by the way, I can also personally attest to the strength of your early go to market team having met a bunch of them.

不过我也得说，不少决策者都跟我提过，他们选择Decagon的原因有很多，其中一个重要原因就是想押注你们。

That being said, I don't wanna underplay how many times a decision maker has told me that one of the reasons of many, right, that they're going with Decagon is they wanna make a bet on you guys.

他们甚至会直接说，觉得Decagon的创始团队是反应最快的。

They're they'll literally say, we think the founding team of Decagon is gonna move the fastest.

这个市场节奏非常快。

This is a very fast paced market.

每周，甚至每天都在变化。

It's changing on a weekly, if not daily basis.

我们觉得你们能看清全局，做出正确的决策，因为这根本不是那种‘一年后或者半年后一切还跟现在一样’的市场。

And we think that you guys are gonna look at the chess pieces and make the right moves because it's not sort of like a, oh, everything's gonna be the same in, you know, a year or even six months type of market.

所以我很好奇。

So I'm curious.

我的意思是，别把优势都让出去了，但你们作为创始人，平时花多少时间在销售上？这个比例有变化吗？

I mean, don't give up too much alpha here, but how much time do you guys spend on sales as founders, and how has that evolved over time?

我大概有80%的时间都在做销售相关的事。

I probably spend most of my time, like 80% maybe.

对。

Yep.

明白了。

Okay.

而且我觉得，关键还是得推动速度。

And, yeah, I think a lot of it is just pushing speed.

一方面，作为创始人，你必须亲自参加客户会议，毕竟客户都希望见到创始人。

So some of it is like, hey, like, as one of the founders, you just have to be in calls and, like, you know, people wanna meet the founders.

另一方面，我也得当那个主要推手，逼着团队提速，同时推动合作伙伴加快节奏。

But also, it's just how can I be the sort of the main force that's pushing our team to go faster, but also just like the partnership to move faster?

我们现在跟好几家全球最大的银行、航空公司和电信公司合作，但不管多快，这些大机构本身流程就慢，事情还是得花时间。

One of the things with that is like, you know, now we work with several of the largest banks in the world, and airlines, and and telcos, and no matter how fast you go, like, there's all there's it's those are still, like, massive organizations, and it will take time.

所以我们一直努力把项目拆解成小块来做。

And so one of the things that we really try to do is, you know, kind of take the project and piecemeal it.

我们不会一下子在所有领域、所有场景全面铺开。

So we're not just deploying across every surface area, every use case at once.

不如先挑一两个最重要的场景，先拿下一个成功案例。

Let's really just pick, you know, one or two of the top use cases and just get a win there.

我觉得这招确实挺管用的。

And I would say that's that's been helpful.

对吧？

Right?

你要是想在一家大银行推进，不可能一上来就搞定一笔大单子。

As you navigate, like, a big bank, like, it's it's not gonna be like a quick, like, you know, you just close a big deal.

没错。

Right.

至少对我们来说，不是我一个人说了算。

At least for us, it's not me.

也许有些人能做到，但说实话，这并不容易。

Maybe some people can do it, but it's yeah.

这仍然需要大量精力，而且耗时很长。

It's still a lot of effort, and it's and it takes time.

所以你得从流程、组织和产品多个角度去设计，不断缩短周期，找到巧妙的办法，而这往往需要创始人亲自参与。

And so you have to figure out ways to, design things from a process perspective, and an org perspective, and a product perspective as well, that like just keep shortening that and like finding clever tactics, and that kind of takes founder involvement.

你不可能指望销售团队不断去调整各种配置，毕竟他们不负责产品本身。

You wouldn't really expect a, like, a a sales team to just, like, constantly be coming up with new configurations there, because, you know, they're not responsible for the product.

他们也不负责公司整个端到端的流程。

They're not responsible for, you know, the end to end, you know, process that the company runs.

所以我觉得，让创始人直接参与这一点非常关键。

So having the founder involved there is very important, I would say.

至于销售团队，他们的任务是专注于销售执行，比如培养内部支持者、打通组织关系等等。

And then the salespeople, their job is to kind of execute on the sales side, you know, like build champions and navigate the org and so on.

另一个重要原因是要花大量时间接触潜在客户和现有客户，因为市场变化实在太快，我们必须迅速搞清楚，目前我们还没在做的那些事情中，有哪些是关键的

The other reason to also spend a ton of time with both sales prospects and existing customers is also because the market is changing so quickly, being able to really quickly understand what are the things that we are not doing today that

我们确实应该这么做。

we should be Yes.

得赶紧做起来。

Doing very quickly.

对吧？

Right?

因为模型的能力一直在变。

Because model capabilities are changing all the time.

新模型一上线，人们就会发现新的用法。

As models roll out, people are seeing other things that happen.

他们会说，哇，这太棒了。

They're like, oh, wow.

这个功能真厉害。

This was really cool.

我注意到你们这边还没做到这一点。

And I'm seeing this here, and you guys aren't doing that as well.

所以，我们必须紧紧抓住这个反馈循环，因为这一点我们绝对不能放手。

So being able to stay really, really close to that feedback loop because that's something we never want to never wanna let go of.

对。

Yeah.

你能不能聊聊这怎么影响了产品路线图？

Do do you wanna talk about how that, like, informs product roadmap?

大概两年前，Decagon 的客户几乎都只关注客户服务。

Meaning, probably, like, two two years ago or so, most of Decagon's customers were focused purely on customer support.

但现在，我觉得情况已经不一样了。

And nowadays, I would say that's, like, that's actually not true.

你们的定位已经扩展成主要为客户提供 AI 专属助手服务。

You guys have broadened your vision to mostly to be like an AI concierge for your customers.

能不能说说这两者之间到底有什么区别？在实际操作中，这对你们的销售团队和产品团队意味着什么？

Do wanna talk about what the distinction between those two actually is and in practice, what that means for both your sales teams and your product So,

我们最初推出的产品功能，都是针对客户服务场景的。

when we launched the original set of use cases that we sold were in customer support.

原因很简单，首先，这是我们早期很多客户面临的主要难题之一。

And the reason was because one, that was one of the biggest challenges that a lot of our early customers were facing.

其次，当时模型的能力也就到这儿了。

And two, that was where the capabilities of the models ended at the time.

那时候，它们能做的事情基本就限于这些了。

That is pretty much the limit of what they were capable of doing.

但现在不一样了，模型变强了，我们的客户也开始想：为什么我要用两套不同的系统，一套只在客户出问题时才用，另一套却在他们想买东西时才启动？

Now, however, as models have gotten better and our customers have realized, well, why would I have one set of models that just learns about my customers when they have a problem and something else when they come to me to buy something?

我们有个客户，最初是跟我们合作做客户支持的。

And so we had a customer that we originally went live with them for customer support.

后来他们发现：你对我们产品了解得真不少啊。

And then they realized they were like, well, you know a lot about our product now.

因为你得懂产品功能，才能做好客户支持。

You know about the capabilities that it has because you need to do that for customer support.

你还清楚我们平时怎么跟客户沟通，以及我们的品牌调性。

You know how we like talking to our customers and our brand.

你们能帮我们处理入站销售吗？

Can you help us with inbound sales?

对吧？

Right?

有人联系进来时，回答他们关于我们的问题，做初步了解，如果商机足够大，就转给对应的企业销售代表。

When someone comes in, answer questions about us, do some discovery, and then assign it to the right enterprise rep if it's a deal of large enough value.

我们还有另一个客户，一开始是用我们来处理各种运营流程。

We had another customer that started using us for a lot of operational workflows.

一旦我们发现客户账户出现任何问题，就能主动联系他们。

So we are able to now proactively reach out to them once we start seeing any kind of issues on customer's account.

说到底，我们从一开始就刻意打造的，并不是一个擅长客服的助手，而是一个能高效执行业务流程的助手。

Because ultimately, at the end of the day, the thing that we built, and we kind of built this intentionally from the start, was not an agent that does customer support well, but rather an agent that follows business process well.

对吧？

Right?

无论是执行运营流程、筛选销售线索，还是回答客户支持问题。

And executing on operational workflows, doing sales lead qualifications, answering customer support questions.

归根结底，它就是一个按照业务流程办事的助手。

At the end of the day, it's just an agent following a business process.

我们当初设计的时候就留了足够的灵活性，因为意识到——嘿，模型迟早会进步，而它们确实进步了。

And we kind of built it flexibly enough to kind of do all these things because we realized that, hey, at a certain point, the models are gonna get better, and they have.

具体来说，是哪些方面的提升让

And what do they specifically get better at that allows

你能够做到

It you to do

就是更好地遵循指令。

is specifically the ability to follow instructions well.

以前的模型，

So when you had models,

比如说几年前，

let's say a

你得给它非常非常精确的指引，不能有丝毫偏差。

few years ago, you'd have to give it very, very tight guidance, very specific instructions that you didn't want it to deviate from.

随着模型越来越聪明，你可以给它更宽泛的指导、更宏观的指令，然后放心地相信它能像人一样理解意图，自己补上那些没说清楚的地方。

And as the models got smarter, you could kind of give it broader and broader guidance, bigger and bigger instructions, and just trust that the models have good enough sense to interpret it like a human would and kind of fill in any missing gaps.

对吧？

Right?

比如在客服场景里，你只需要给模型一条明确的流程，这就足够了。

Because for, again, for customer support, you can have a very tight path that a model should follow, and that's all you really need.

但在销售初步筛选时，你更想问一些开放式的探索性问题。

Whereas for sales qualifications, you kinda wanna ask open ended discovery questions.

对话会自然地来回推进，所以你需要模型能合理地填补那些空白。

The conversation is gonna kinda bob and weave, so you need the model to kinda fill in with reasonable things.

所以这些年，模型真正提升的，就是这方面的能力。

So that's specifically kinda what the what the model got better at over the years.

是的，如果你想想未来十二个月的产品路线图，现在变化这么快，真正的答案其实是：我们只能边看边走。我们当然知道现在在做什么，但现实是，在今天的AI环境下，想精确规划十二个月的路线几乎不可能。

Yeah, I think if you think about, you know, what is the like twelve month product roadmap, because things are moving so fast, like the answer, the real answer to that is like, you know, we kind of like see how it evolves, and we obviously know what we're working on now, but I think realistically in today's AI world, it's very difficult to have like a twelve month road map to a tee.

你可能大概知道想做哪些方向，但既然现在开发这么快，有想法就赶紧做出来，别等。

You maybe know how like some themes of what you wanna build, but ideally, if you have those things, just build it like right now, because it's so fast to build things now.

所以我觉得，这只是一个方面，但我们的长期愿景依然非常清晰——我们用‘专属管家’这个词，其实意思就是，AI代理应该成为你企业、你品牌的入口，所有与客户的互动，无论是被动响应还是主动发起，都该由AI来处理。

So I would say that that is that is one element, but then the sort of long term vision is still very clear to us now, right, which is we use the term concierge, but really it just means like, hey, an AI agent should just be the front door of your business, of your brand, and every interaction, whether it's reactive or proactive with a customer, should be handled by AI.

我们已经看到AI在这一点上非常出色，比如客户服务就是核心部分，主要处理大量客户主动发起的请求，但为什么不能让它也做其他事情呢？

And we've already seen that AI is very good at that, right, customer service is a huge pillar of that, where it's all these inbound interactions, but why not have also be able to do all these other things?

所以随着时间推移，我们并不是自己闭门造车去猜这些需求该是什么。

So over time, again, we're we're not trying to figure out on our own what these things are.

现在我们已经有大量客户主动给我们反馈，告诉我们在意什么，我们就根据这些反馈来决定开发什么。

We kind of have now have a lot of customers that will give us signal on the things that they care about, and those will be the things that we build.

我们已经聊了很多关于模型能力不断提升的事，当然，你们的进展甚至比模型进步还要快。

We've we've talked a lot about how the models are getting better and, you know, obviously, your capabilities are are moving along even, you know, ahead of that progress.

目前你们觉得最大的瓶颈是什么？

What are some of the bottlenecks right now that you're seeing?

不管是能力层面的限制，我也说不准。

Whether that's on the capability side, I don't know.

也可能是像持久记忆这类问题。

It could also be, you know, around persistent memory.

不知道你们觉得现在这情况是否已经达标，或者还有其他瓶颈。

I don't know if you guys feel like that's kinda up to snuff on where you would like it or could be other bottlenecks.

我挺好奇的，你们希望看到哪些改进，目前是什么在拖后腿？

But curious, like, what what would you like to see and what's kinda holding you back?

招人难。

Hiring.

明白了。

We Got it.

所以问题不在AI这边。

So it's less on the AI side.

其实更像是你

It's actually like You

知道，我们人类。

know, we people.

我们对token的需求量巨大，但我们始终更渴望优秀的人才。

We are we are voracious consumers of tokens, but we've always loved more great people.

我觉得

I think

现在有太多事情可以做了。

there's so

现在有太多事情可以做了。

much to build these days.

是的。

Yeah.

说白了就是。

It's you know?

那你们为什么招不到人呢？

And why can't you hire people.

为什么不用AI代理来干你们现在做的事呢？

AI agents to do the things that you're doing?

是的。

Yeah.

我们现在对令牌费用的消耗非常大，但费用低的反而更划算。

We are voracious consumers of of token bills are better than large.

不过还是有一些事情，我觉得我们还没到能让AI代理决定该做什么、判断是否完成的地步。

But there there are still things I I don't quite yet think we're at the point where we can have the AI agents make decisions on what to build and kinda have that the taste of, is this done yet?

对。

Mhmm.

对吧？

Right?

我们可以把很多具体执行的任务外包出去，但我还不觉得它们能决定该做什么、不该做什么这类战略选择。

So we can outsource a lot of specific execution steps, but I I don't yet think they're at the point where they can make the call on what to build, what to exclude, things like that.

模型进步了不少，自从你三年前创立公司以来，这对你的招聘需求有影响吗？

So the model's improving, have they since you founded three years ago, has it changed your hiring needs at all?

很多人说，现在一个人就能打造一家独角兽公司。

Because a lot of people are like, oh, you can build one person unicorns.

想

Think

这个说法确实经常被提起，但我认为一个简单的反例就是，所有AI编程初创公司都在疯狂招人。

this argument does get tossed around a lot, but I think an easy counterexample to this is all the AI coding startups are hiring like crazy.

他们应该是这些模型最精通的使用者，却还在拼命招聘。

They're like the most sophisticated users presumably of these models, they are hiring like crazy.

我觉得原因很简单，因为人人都能用上这些工具。

And I think the reason is just because everybody has access to these tools.

如果竞争对手用它们来开发更多功能，那我们也得加快脚步，多做点东西。

And so if our competitors are going to use them and build more things, we need to build more things.

如果别人说：‘这是我们的路线图，现在能用三分之一的时间完成’，然后就停止招聘了，那我们只会想：哇，原来我们也能用三分之一的时间搞定。

If somebody else said, Oh, here's our roadmap, and now we can get through it in a third of the time, and then they just stop hiring, we would just take that to mean, Wow, we can get through it in a third of the time.

那太好了。

Great.

干脆多做三倍的工作量。

Let's build three times as much stuff.

结果发现，大家都算的是同一笔账。

And turns out everybody does the same calculus.

所以大家都得继续招人，还得加快节奏。

So everybody both needs to keep hiring more and shifts more.

这对模型的使用者来说当然是好事，但我觉得这并没有真正改变我们的计划。

So it is great for consumers of these models, but I don't think it has materially changed our our our plan.

这真有意思。

It's so funny.

我还以为你会说点别的，比如语音模型的延迟之类的问题。

I thought you're gonna say something like, I don't know, latency of voice models or something like that.

但看起来在技术层面，

But it seems like on the technological

我的意思是，现在还有些东西，

I mean, there's

在瓶颈方面，确实是这样。

bottleneck side, it's Yeah.

我们还在等一些技术突破。

There's still stuff that we're waiting for.

对吧？

Right?

没错。

Like Yeah.

语音到语音模型是个很有趣的前沿领域，目前还在持续研究中。

You know, voice to voice models is an interesting frontier that there's still research happening on.

让更小的模型一出来就更聪明，这方面的进展也很有意思。

You know, the getting smaller models to be smarter, smarter out of the box.

对吧？

Right?

所以，我们还在密切关注一些即将出现的新进展，这些对我们很重要。

So there's there are gonna be still developments that we are watching closely that we care about.

对。

Mhmm.

但从商业角度看，重点不在模型本身，而在于你能不能快速把公司做起来。

But from a business perspective, it's less on the model side and more on the, just like, can you build the company fast?

是的。

Yeah.

实际上，既然我们谈到招聘了，我觉得‘ grind slop ’最近在 X 上成了个热门话题。

Actually, maybe since we're on the topic of hiring and, you know, I feel like grind slop has become this theme on on x.

这挺有意思的，作为投资人，我得承认，你们可是出了名的每周六七天都在办公室里泡着。

And it's so funny because, you know, as a as a VC, like, I'll I'll admit, you know, when I I mean, you guys are famously in the office six, seven days a week.

希望这别被贴上‘ grind slop ’的标签啊。

I hope this doesn't get marked as grind slop now.

但我觉得，持续为团队和客户拼命，这本来就是该做的。

But, you know, I think constantly hustling for your teams and your customers.

看到这种说法被完全颠倒过来，真是挺讽刺的。

And it's so funny to see that kinda turned on its head.

所以我很好奇，你对现在外面流传的这种说法怎么看？

And so I'm just curious, what is your what are your thoughts on the narrative out there?

‘GrindSlop’ 这个词太笼统了，但你们确实很拼，办公室里也充满默契和干劲。

And, like, GrindSlop is such a general term, but, like, you know, you guys grind, but there's also a lot of camaraderie and excitement in the office.

所以，能不能再多说说，你们是怎么打造这种文化的？

So, like, just, you know, say more about that and, like, how you're building the culture.

对。

Yeah.

‘Grind Slop’这个词其实挺有意思的。

I mean, Grind Slop's a funny term.

我们从来没把这叫‘Grind Slop’，因为我们觉得拼命工作不是为了熬日子。

We so we have never posted Grind Slop because we kind of view that working hard is just like, I don't think people are working hard to grind.

就是觉得事情太多，得干。

Just like, oh, there's a lot of stuff to do.

大家待在办公室，主要是因为觉得这是人生中特别有意思的一段时光。

People spend time there mostly because like, hey, it's, you know, this is like a fun time of our life.

我们想充分利用自己的能力和潜力，别让它们白白浪费了。

Like, we wanna take advantage of our talent and sort of potential so that it's not wasted.

我觉得这才是最主要的原因。

I think that's that's the main reason.

这其实是一种很自然的延伸，我们的核心目标就是能不能做出好产品，

And it's a very sort of like, it's like many orders from like the main goal, which is like, can you build a good product,

能不能在这一行里胜出，

and can you

而这个目标带来的一个结果就是，大家会更努力地工作。

win in a space, and but it's like one of the effects of that is that people work harder.

从一开始，我们确实有办公室文化，但从来不会强制要求大家周末来上班。

And even from the from the beginning, like, yes, we have an office culture, but, like, we're never mandating people to come in on the weekends.

我们根本不关心他们每天在办公室待多久。

We don't really care how long they are they are in the office.

大家待在办公室，主要是为了方便沟通，提高效率。

It's just people are in the office just so that, like, we can maximize communication.

我们就是这么看的。

And that's how we view it.

我觉得我们自己并没什么特别拼命或者特别野心勃勃的。

Like, I don't view ourselves as, like, you know, abnormally grindy or abnormally ambitious.

我觉得吧，我身边都是特别有抱负的人，比如阿什温那些人，久而久之，努力工作就成了一种常态，大家都这样，也就没什么特别的。

I think, like, we're I think we just like, I have a lot of ambitious people around me, like Ashwin and all these people, so they kinda just become normal and like everyone works hard, so it's just kind of like normal.

所以我觉得这并没什么了不起的，也不是我们特意标榜的东西。

So I don't think it's like something that we view as, you know, something like super special.

而且我们也把很多工作看作是团队合作的事。

And we kinda also view a lot of what we do very much as a team sport.

对吧？

Right?

我们很少在部门之间划清界限，经常能看到工程师参与早期的销售电话。

Where we very rarely have lines between our orgs in that you will very commonly see engineers on early stage sales calls.

也能看到销售同事帮忙调试产品问题。

You will see salespeople debugging parts of the product.

我们的项目管理团队更是贯穿了从头到尾的各个环节。

You will see our APM team in absolutely both ends of the spectrum.

所以我觉得，像这样职能差异很大的团队能紧密协作，一方面确实需要大家在办公室里，因为大家经常一起头脑风暴。

And so I think being able to have teams that are so kind of disparate from a function perspective all working together, one, kind of needs people in the office because everybody's kind of jamming on ideas together.

但另一方面，这也让工作变得特别有意思，因为大家都在为同一个明确的目标努力。

But two, it also makes it fun in a way because everyone is working together towards some very specific outcome.

要么就是赶在交易成交前把产品做出来，要么就是上线一个新功能。

It's either building this thing for this customer in time for the deal to close or launching this new thing.

因为这么多不同团队都在一起协作，大家心里都清楚，咱们得齐心协力把这事搞定。

And because it's so many different teams working together, it's kind of a it's kind of a we're all in this together to get this across the line.

对。

Yeah.

不过你们最近开了这么多新办公室，这种情况怎么维持下去呢？

And how does that scale as, you know, you've you've launched a lot of new offices recently.

澳大利亚和伦敦都新开了办公室。

You have a new Australia office, London office.

纽约办公室的增长更是快得惊人。

Your New York office is growing like crazy.

对。

Yeah.

你们都在旧金山的时候，怎么保持这种文化呢？

How do you maintain that culture when you guys are both in San Francisco?

我不知道。

I don't know.

说实话，确实如此。

I mean yeah.

我不是说不

I don't don't

这对我们来说还没找到终极解法。

think it's a solved problem for us.

我们一直在不断努力改进。

Like, we're constantly working on it.

每次公司迈上一个新台阶，都得推出一些新措施，确保大家真正理解我们的文化，明确责任，而且确实有压力——这本来就应该有。

And then, like, every single time the company gets to the next days, there's, like, new things we have to institute to just make sure that everyone understands the culture, and there's, like, very high accountability, and, you know, there there is pressure because there should be.

前一百号人的时候，这问题不明显，因为大家都互相认识，但随着团队扩大，有些人可能根本没人跟他们讲过公司的愿景或文化。

And so that's something that we're constantly adding on because in the first 100 people, it doesn't really matter because everyone kind of knows each other and, like, everyone's but, like, as you grow, people might not have no one's communicated the vision to them or communicated the culture to them.

而且，没错，本之前跟我们聊过 a16z 的文化。

And, yeah, I know, like, Ben was talking to us about the a16z culture.

对吧？

Right?

那种文化特别注重行动，不能光堆些空洞的口号。

It's like how it's very, like, action oriented, and you can't just, put put, like, fluffy stuff on there.

所以，随着公司发展，我们正努力把这些事情做得更好。

And so, yeah, it's all these things that we're trying to do a better job of as we grow.

另外，我们每招一个人，都会让他们刚入职时来旧金山待几周。

The other thing is also we bring everybody that we hire out to San Francisco for a couple weeks when they start.

让他们直接浸润在最初那种文化氛围里。

So they're immersed in the kind of the original kind of soup of culture.

然后每个新办公室——

And then for every new office.

现在纽约和伦敦这些办公室都已经足够大，本身就能传承原有的 Decagon 文化了。

At this point, New York and London, all these offices are big enough that they have the original Decagon culture there anyway.

但对于全新的办公室，我们会派来自这些核心枢纽的人过去待上几个月，直到当地团队足够壮大，形成自己的文化氛围，避免与最初的Decagon文化偏离太多。

But for brand new offices, we actually have people from one of these hubs go out and spend a few months there, really, until the office becomes big enough and has its own kind of culture so that it doesn't kind of become too different from what kind of the original Decagon culture was.

说到国际化，我们去年请来了拉古拉姆，就是前VMware的CEO。

Actually, on the topic of international, we're so we brought on Raghuram last year, obviously, CEO of VMware.

除了投资，他还帮我们大力拓展了国际业务能力。

And in addition to investing, he's helping us build out a ton of our international capabilities.

我们发现，我们的AI公司比以往任何时候都更早地走向国际市场。

And I think one of the things that we've seen is that our AI companies are just getting pulled internationally way earlier.

比如你开澳大利亚办公室，按理说还太早了，但因为你已经有客户主动找上门了。

Like, for you to have an Australia office, it seems premature except for the fact that you have customer pull.

你现在的规模，跟你成立的时间相比，已经大得惊人了。

Your scale is quite large for, you know, how long ago you were founded.

不过你能不能再多讲讲这个？

But maybe say more about that.

你的产品在这些其他地区也适用吗？

Like, does that translate like, does your product translate well to these other geos?

企业方面的顾虑是更多了，还是更少了，还是跟美国市场差不多？

Are there more enterprise concerns or fewer, or is it kinda just comparable to The US market?

是的。

Yeah.

我觉得有两个趋势。

I think there's two trends.

一个是，AI现在太火了，每个客户都试过ChatGPT之类的工具，所以董事会和高管层都在施压，催着赶紧上马一些项目。

One is that AI is just such a phenomenon that every buyer out there has, you know, tried ChatGPT or whatever, and so there is a lot of just top down pressure from boards and c suites to just get moving on something.

如果你想想这些公司，他们都在问：我们该怎么用上AI？

And if you think about a lot of these businesses, they're like, how do we adopt AI?

答案往往是，先搞几个代码助手，再弄个客服机器人，因为这些是最容易上手的场景。

Well, it's you know, let's do some coding agents, and, like, let's do customer service, because those are, like, the oddiest ones.

所以，正如你所说，确实有很强的推动力。

So there is a lot of pull to to your point.

另一个趋势是，AI相关的沟通门槛低多了。

And then the other trend is that the language is a lot easier with AI.

所以

So

对的。

Mhmm.

是的。

Yeah.

过去，可能一个障碍是，我的语言或者我的应用不支持德语之类的。

In the past, maybe a blocker would be, oh, my language just doesn't work in or my app just doesn't work in German or Right.

某种特定的语言，对吧？

A particular language, right?

但现在要让应用适配起来容易多了。

But now it's a lot easier to adapt your app.

所以我觉得，正因为这些原因，国际化进展快了不少。

So I think for those reasons, international has been a lot faster.

但与此同时，我们也得确保别摊得太薄，所以这中间其实有个平衡，到底怎么做才最合适，现在还说不准。不过我们已经大致决定了，哪些市场值得我们重点投入，一旦决定投，就得真投入。

At the same time, right, like we also want to make sure that we're not getting spread too thin, and so it's kind of this balance where it's like very unclear what the perfect answer is, but you know, we've kind of made a determination of like which markets we're okay with really investing in, and if we're gonna invest, we're gonna, you know, really invest.

一般来说，这些市场都是我们从美国办公室自然吸引到一些客户的地区。

And generally, markets are ones where we've already just, like, naturally picked up some customers out of The US office or something.

是的。

Yeah.

现在是时候派人过去了。

And then now it's, like, time to deploy people.

因为虽然现在有这些趋势让国际化变得更容易，但还有很多你平时想不到的问题。

Because, yeah, even though these there are these trends that make it easier to go internationally, you know, there's also other things that you don't really think about.

对吧？

Right?

比如数据本地化的要求。

Like, you have to have data residency.

还有各种本地竞争对手，他们对市场了解得更深，你得好好应对这些挑战。

There's all these things where there's, like, local competitors, which are you just know the market a lot better, and so you have to navigate that well.

是的。

Yeah.

没错。

Absolutely.

你觉得在我们熟知的这些大类别里，本地竞争者还有没有生存空间？

Do you think there is a, like, a space for, like, local competitors to the large categories that we know about?

当然有他们的空间。

I mean, there is space for them.

关键在于，从长远来看，会不会出现整合？

I think the question is, long term, is there consolidation?

无论是地域、行业，还是细分市场，都有可能走向整合。

You could see that for both geographies, but also verticals, and then also market segments.

总会有人在不同领域找到自己的细分市场。

So there are gonna be people that find their niche in different places.

我们的看法是，我们之所以选择做得很横向，是因为我们认为，在我们的领域里，最终胜出的会是横向平台。

Like, our view, the reason why we've kind of built so horizontally is that we believe that in our space, the winners are gonna be horizontal.

真正高度垂直的解决方案很难长期存活，因为这类需求并没有那么强烈。

There's just not that much that is, like, super verticalized that where, like, you could see a a a pure vertical solution surviving.

而且在我们这个领域，历史上一直都是这样。

And historically, that's just been true in our space.

对吧？

Right?

比如Salesforce，就是非常横向的，我们也是完全横向的。

Like, Salesforce, very horizontals, and this is very horizontal.

所有这些解决方案都是横向的，因为你从规模和强大而深入的产品中获得的收益，远超过那些只做垂直特定功能的。

Like, all these solutions are very horizontal because you you just gain more from having that scale and having a very robust and deep product than you do from, like, having very vertical specific features.

随着时间推移，我们也会慢慢加入一些垂直功能，但我们的看法是，从垂直领域和市场角度看，最终会走向整合。

And over time, we're also gonna build, you know, vertical features into it, but our view is that, like, from a vertical and sort of market point of view, there will be consolidation.

说到整合，我想聊的不只是地理上的整合。

Just sort of on related to consolidation, but maybe not just geographic consolidation.

我很好奇，如果AI助手真的成了企业与客户之间的主要接口，那CRM和这些传统上至关重要的客户数据系统，会怎么演变呢？

I'm curious if AI concierge becomes really the interface between the business and its customers, do you see what like, how does CRM and all of these, you know, sort of traditional data very important systems of record, right, for the end customer?

在这样一个新世界里，它们会变成什么样？

How do those evolve in this new world?

那在你自己的角色上，你看到有类似的整合趋势吗？

And do you see any consolidation there in terms of your own role?

是的。

Yeah.

我觉得这不一定是非此即彼的关系。

I don't think it's necessarily an either or.

对吧？

Right?

毕竟，我们在这里做的，是让这些成本效益更高的工具变得更容易获得。

Because ultimately, what we're doing here is trying to democratize access or availability of these cost COGs, right?

如果你是一家每年花十万美金的客户，你自然会得到最个性化的服务。

Which is if you were going to a business where you were spending $100,000 a year, you would get the most personalized treatment.

他们会清楚地了解你是谁，你的偏好是什么。

They would know exactly who you are, what your preferences were.

他们希望帮你选品，比如。

They wanna help you shop, for instance.

他们会为了你把整个店都关掉。

They'll shut the whole store down for you.

其实我不确定他们会不会为这点钱这么做。

Actually, I don't know if they do that for dollars.

但你明白大概的意思了。

But you get the general flaw.

是的。

Yeah.

你以前从来没这么试过。

You've never tried that before.

你以前从来没这么试过。

You've never tried that before.

但如果你在一家只花10美元的店，他们就没法这么做，因为这样不划算。

However, if you're at a business where you're spending $10 they can't do this for you because they can't make it economical.

对吧？

Right?

没错。

Yep.

这并不是他们不想为顾客提供这样的服务。

It is not a lack of desire to do this for their customers.

对。

Right.

只是单笔交易的经济模型支撑不了这种做法。

It is just that the unit economics don't support that.

所以本质上，我们只是在说：假如你连10美分都不愿意给，突然间只要成本变得划算，他们就愿意做了。

And so effectively, all we're doing here is saying, okay, if you couldn't give them that experience for 10¢, all of a sudden it is economical and they'd wanna do that.

那么，回到刚才的问题：这是否意味着客户关系管理系统会消失？

Now, to the point of, does that mean CRMs go away?

我的答案是否定的，因为如果现在一家公司雇的是真人管家，他们依然会把客户信息录入CRM，以便日后追踪。

I mean, my answer is no, because if you had a company today where your concierge is a human being, they still write your info in a CRM so that they can track it for later.

我觉得，当我们用上AI助手时，它们同样需要一个地方来存储这些信息。

And I think when we have these AI agents, they will need somewhere to put that information.

所以我不觉得这类辅助软件会消失。

So I don't necessarily think those kind of auxiliary pieces of software go away.

因为我们做这些聚合器，核心目标其实是怎么打造更好的体验。

Because for us, as we're building these concentrators, our goal really is how do we create that great experience?

我们还是需要地方来存储这些数据。

We will still need places to put that data somewhere.

对吧？

Right?

是的。

Yeah.

我觉得CRM其实会发展得不错。

I actually think CRMs could do quite well.

它们会有些不同，因为CRM本质上就是一种数据库。

They'll they'll be slightly different in the sense of CRMs are kind of databases in a way.

没错。

Mhmm.

人们有时候对它们感到沮丧，其实并不是因为界面太难用之类的。

And, you know, the the frustration people have with them sometimes is that the interfaces are not really difficult to use, etcetera.

但未来，可能是智能代理直接在这些界面里操作，而且你

But in the future, maybe the agents are just using the interfaces, and, you

最后一点，我们刚才录之前聊到，你们自己其实做过一些小的AI实验，就是想看看怎么让自己的生活更高效。

Maybe last thing is we were chatting right before we we started recording that you guys have done some little AI experimentation on your own as you just figure out how to make your own life more productive and effective.

Ashwin，听上去你做了些挺有意思的东西。

Ashwin, it sounds like you've done something kind of interesting.

是的。

Yeah.

我觉得过去几年里，这些模型变得越来越聪明了。

You know, I think the models have gotten very smart over the last several years.

我和他经常用它们来头脑风暴，想点子。

And, you know, the two of us will often use it to brainstorm ideas.

没错，它们确实特别擅长提出很棒的主意。

Right, because they are genuinely very smart at coming up with great ideas.

不过我发现，就我做的这些工作而言，真正的瓶颈在于业务背景。

However, the bottleneck, I realize at least for a lot of the work that I do, is business context.

对吧？

Right?

每个想法背后都有大量的背景信息，比如我们有哪些限制、目标是什么，这些内容每次都要重新向AI解释，真的很麻烦。

There's a lot of context for every idea around, okay, the constraints that we have, the goals that we're going for and things like that, that is difficult to re explain to the agents every time.

所以我花了不少时间，给自己搭建了一些AI代理，专门用来完整地保存这些业务背景。

And so I actually spent a while building agents for myself to capture all the business context well.

它就像一直站在我身后，不断整理和更新信息，比如：我们已经招了哪些人。

So it just kind of looks over my shoulder all the time and is constantly compiling context on, oh, here are the people that we've hired.

我们还需要招哪些人。

Here are the people that we need to hire.

我们现在在推进哪些项目。

Here are the deals that we're working on.

还有我们正在面对哪些问题。

Here are the problems.

这是我们目前面临的挑战。

Here are the current challenges that we have.

这样以后我就可以直接去查看，说：嘿，我们正在考虑招这个人。

So that later on, I can just go to it and say, hey, there's this new person that we're thinking of hiring.

你觉得怎么样？

What do you think?

现在它已经能自动充当备忘录了。

And now it's able to automatically be the notebook.

我们之前有两个候选人非常相似，他们都有些类似的短板或欠缺的技能。

Well, we had two candidates that were very similar, and these candidates had these kind of drawbacks or skills that they didn't have.

如果我们招了这个人，那就又会多一个拥有同样问题的人。

And so if we hire this person, it's gonna be another person that has those kind of exact same things.

所以我们需要一个能互补的人。

So we need someone complimentary.

所以这个人可能并不是最合适的选择。

So this is probably not the best person to have.